Translating the Clinical Knowledge of Mendelian Diseases to Real-world EHR Data to Improve Identification of Undiagnosed Patients

项目总结 在过去的二十年里，在成本、可获得性和可解释性方面取得了非凡的进步 基因测试。在这一惊人进展的背景下，令人震惊的是，对于许多罕见的遗传病来说， 诊断延迟--症状出现和诊断之间的时间--并没有改善。当前健康状况 护理服务机构无法有效地确定哪些患者将从基因测试中受益最大。结果, 许多受遗传病影响的患者在症状出现后数年才被诊断出来，或者从未被诊断出来。 完全被诊断出来，导致昂贵的诊断奥德赛，遗传服务中的医疗保健差距，以及 对于那些有有效的、有针对性的治疗的人来说，可预防的发病率和死亡率。 我们对遗传病的许多了解都是基于对个人及其家庭的研究。这 已被证明是一种有效的方法来识别遗传病的临床特征， 医学遗传学中最持久和最有用的资源之一：人类在线孟德尔遗传 (OMIM)。然而，OMIM中的临床描述并不总是与真实描述疾病的方式相匹配- 世界电子病历数据。为了提高我们有效使用基因检测的能力，我们可以从数据中大规模地学习 在测试和诊断患者时被临床捕获。EHR为研究遗传学提供了机会 从新的角度看待疾病，支持可扩展的方法，以增强现有的知识库 包括在真实世界卫生保健数据中观察到的表型。 这项建议建立在我们之前的工作基础上，该工作管理来自电子健康记录的基因测试数据和开发工具 从典型的遗传性疾病特征中识别未诊断的患者。具体地说，我们构建了一个 从电子病历中提取的超过20,000人的临床基因检测信息数据库，包括 关于检测结果、变体解释和诊断的详细信息。从这个资源中，我们可以 定义基于EHR的病例系列具有经基因确认的遗传病临床诊断的个人。 我们将使用数据驱动的方法从基于EHR的案例系列中识别特征表型，以及 将这些结果与OMIM的临床描述合并。这种方法寻求翻译经过精选的 将OMIM中的耐用知识编目为可移植和可扩展的产品，该产品可以基于任何一组EHR 以确定未被诊断为遗传病的患者。 该建议的最终目标是利用这些数据和工具来1)翻译和添加到临床 使用真实世界的EHR数据对遗传病进行治疗，2)评估基于EHR的工具的诊断效率，该工具 确定未诊断的患者和3)确定人口统计学和表型特征的贡献 导致更早或更晚的诊断。

PROJECT SUMMARY The last two decades have seen extraordinary advances in the cost, accessibility, and interpretability of genetic testing. In the context of this astonishing progress, it is striking that for many rare genetic diseases, diagnostic delay – the time between onset of symptoms and a diagnosis – has not improved. Current health care services are unable to effectively identify patients that would benefit most from genetic testing. As a result, many patients affected by genetic disease are not diagnosed for years after symptoms develop, or are never diagnosed at all, leading to costly diagnostic odysseys, health care disparities in genetic services, and preventable morbidity and mortality for those with conditions that have an effective, targeted treatment. Much of what we know about genetic disease is based on studies of individuals and their families. This has proven to be a powerful method for discerning the clinical characteristics of genetic disease, generating one of the most enduring and useful resources in medical genetics: the online Mendelian inheritance in man (OMIM). However, clinical descriptions in OMIM do not always match the way diseases are described in real- world EHR data. To improve our ability to use genetic testing effectively, we can learn, at scale, from the data clinically captured while testing and diagnosing patients. EHRs provides an opportunity to study genetic disease from a new perspective, enabling scalable methods that augment existing the knowledge base to include phenotypes observed in real-world health care data. This proposal builds on our prior work curating genetic testing data from the EHR and developing tools to identify undiagnosed patients from characteristic genetic disease profiles. Specifically, we have built a database of clinical genetic testing information extracted from the EHR for over 20,000 individuals, with detailed information regarding test results, variant interpretation, and diagnosis. From this resource, we can define EHR-based cases series of individuals with genetically-confirmed clinical diagnoses of genetic disease. We will use a data-driven approach to discern characteristic phenotypes from the EHR-based case series, and merge these results with clinical descriptions from OMIM. This approach seeks to translate the curated, durable knowledge cataloged in OMIM to a portable and scalable product that can layered on any set of EHRs to identify undiagnosed patients with genetic disease. The ultimate goals of this proposal are leverage these data and tools to 1) translate and add to clinical curations of genetic diseases using real world EHR data, 2) assess diagnostic yield of EHR-based tools that identify undiagnosed patients and 3) characterize the contribution of demographic and phenotypic features that lead to earlier or later diagnosis.